(2014). Exploiting social features for improving cognitive radio infrastructures and social services via combined MRF and back pressure cross-layer resource allocation. Computational Social Networks, 1(1). https://doi.org/10.1186/s40649-014-0004-1
Περίληψη
The observed unbalance of spectrum utilization combined with the proliferation of wireless devices and the growth of online social networks call for a joint study of cognitive radios (CRs) and social networking towards improving the operation of both the underlying CR-capable communications infrastructures and of the corresponding dynamic networks. In this work, we address such issue and focus on the twofold direction of resource allocation in CRs and social feature exploitation with the aim of providing a holistic cross-layer optimization framework that jointly considers information from the social layer as well as the traditional network protocol layers. We combine Markov random field (MRF) cross-layer decisions with back pressure (BP) features for the allocation of resources, in order to improve the operation of CRs that act as social network substrate. Espousing the recent advances in the field of dynamic spectrum allocation, the proposed BP-enhanced MRF (BPeMRF) network optimization serves the purpose of improving in a low complexity manner the capacity of agile infrastructures, which are required to adapt fast according to the demands of the volatile CR environment and the overlaying dynamic online social networks. In addition, within the proposed socially enhanced BPeMRF (SeBPeMRF) approach, we leverage emerging social information to further enhance performance. The obtained results exhibit the efficacy of BPeMRF/SeBPeMRF regarding the above objectives and demonstrate significant promise for further improving the corresponding infrastructures and services. © 2014, Anifantis et al.; licensee Springer.
- DOI
- 10.1186/s40649-014-0004-1
- ISSN
- 21974314
- Τύπος
- Journal Article
- Έτος
- 2014
Σύνδεσμοι
BibTeX
@article{Anifantis2014,
title = {Exploiting social features for improving cognitive radio infrastructures and social services via combined MRF and back pressure cross-layer resource allocation},
author = {E. Anifantis and E. Stai and V. Karyotis and S. Papavassiliou},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85029244666&doi=10.1186%2fs40649-014-0004-1&partnerID=40&md5=c42a9d50f41455d96e48947cf21d6431},
doi = {10.1186/s40649-014-0004-1},
issn = {21974314},
year = {2014},
date = {2014-01-01},
journal = {Computational Social Networks},
volume = {1},
number = {1},
publisher = {SpringerOpen},
abstract = {The observed unbalance of spectrum utilization combined with the proliferation of wireless devices and the growth of online social networks call for a joint study of cognitive radios (CRs) and social networking towards improving the operation of both the underlying CR-capable communications infrastructures and of the corresponding dynamic networks. In this work, we address such issue and focus on the twofold direction of resource allocation in CRs and social feature exploitation with the aim of providing a holistic cross-layer optimization framework that jointly considers information from the social layer as well as the traditional network protocol layers. We combine Markov random field (MRF) cross-layer decisions with back pressure (BP) features for the allocation of resources, in order to improve the operation of CRs that act as social network substrate. Espousing the recent advances in the field of dynamic spectrum allocation, the proposed BP-enhanced MRF (BPeMRF) network optimization serves the purpose of improving in a low complexity manner the capacity of agile infrastructures, which are required to adapt fast according to the demands of the volatile CR environment and the overlaying dynamic online social networks. In addition, within the proposed socially enhanced BPeMRF (SeBPeMRF) approach, we leverage emerging social information to further enhance performance. The obtained results exhibit the efficacy of BPeMRF/SeBPeMRF regarding the above objectives and demonstrate significant promise for further improving the corresponding infrastructures and services. © 2014, Anifantis et al.; licensee Springer.},
note = {cited By 8},
keywords = {},
pubstate = {published},
tppubtype = {article}
}